Akurasi dalam Analisis Kompetensi Calon Tenaga Keperawatan Menggunakan Algoritma Apriori
Keywords:
Data Mining, Apriori Algorithm, Association Rule, Competence, Nurse
AbstractSetiap lulusan S1 keperawatan (NERS) harus dinyatakan kompeten pada Uji Kompetensi Ners Indonesia (UKNI) yang diadakan Kemdikbud untuk mendapatkan Surat Tanda Registrasi (STR) sebagai syarat bekerja. Tingkat kelulusan mahasiswa keperawatan dalam mengikuti UKNI sangat rendah jika dibandingkan dengan beberapa negara lain. Penelitian ini bertujuan mengidentifikasi faktor-faktor yang perlu dioptimalkan calon tenaga keperawatan dalam persiapan menghadapi UKNI dengan teknologi data mining. Data yang diolah dalam penelitian ini bersumber dari Appskep Indonesia, suatu startup yang bergerak di bidang pendidikan kesehatan, diantaranya try out dan bimbingan belajar UKNI. Appskep memiliki data kelulusan UKNI lebih dari 2000 pesertanya yang tersebar di seluruh Indonesia pada tahun 2021 dan 2022. Data tersebut dianalisis menggunakan Algoritma Apriori untuk menemukan rule-rule asosiasi yang terkait dengan kelulusan calon tenaga perawat dalam UKNI. Hasil dari pengolahan data peserta UKNI ini adalah ditemukannya beberapa set rule asosiasi yang mempengaruhi kompeten atau tidaknya calon tenaga perawat dalam UKNI. Rule asosiasi yang paling dominan adalah jika penguasaan materi keperawatan jiwa lebih dari 60 persen maka seorang perawat akan lulus pada UKNI, dengan support 55% dan confidence 100%. Downloads
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Published
2022-12-31
Section
Articles
How to Cite
Habiburrahman. (2022). Akurasi dalam Analisis Kompetensi Calon Tenaga Keperawatan Menggunakan Algoritma Apriori. Jurnal Informatika Ekonomi Bisnis, 4(4), 180-185. https://doi.org/10.37034/infeb.v4i4.167
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